{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "initial_id",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from torch.utils import data\n",
    "from d2l.torch import get_dataloader_workers\n",
    "import torchvision\n",
    "from torchvision.transforms import transforms\n",
    "import torch\n",
    "from IPython import display\n",
    "from d2l import torch as d2l\n",
    "\n",
    "batch_size = 256\n",
    "\n",
    "\n",
    "def load_data_fashion_mnist(batch_size, resize=None):  #@save\n",
    "    \"\"\"下载Fashion-MNIST数据集，然后将其加载到内存中\"\"\"\n",
    "    trans = [transforms.ToTensor()]\n",
    "    if resize:\n",
    "        trans.insert(0, transforms.Resize(resize))\n",
    "    trans = transforms.Compose(trans)\n",
    "    mnist_train = torchvision.datasets.FashionMNIST(\n",
    "        root=r\"D:\\baidu\\dataset\\Fashin-MNIST\", train=True, transform=trans, download=True)\n",
    "    mnist_test = torchvision.datasets.FashionMNIST(\n",
    "        root=r\"D:\\baidu\\dataset\\Fashin-MNIST\", train=False, transform=trans, download=True)\n",
    "    return (data.DataLoader(mnist_train, batch_size, shuffle=True,\n",
    "                            num_workers=get_dataloader_workers()),\n",
    "            data.DataLoader(mnist_test, batch_size, shuffle=False,\n",
    "                            num_workers=get_dataloader_workers()))\n",
    "\n",
    "\n",
    "train_iter, test_iter = load_data_fashion_mnist(batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 3.6.1. 初始化模型参数¶"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "bd45f8e595bb9856"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "num_inputs = 784\n",
    "num_outputs = 10\n",
    "\n",
    "W = torch.normal(0, 0.01, size=(num_inputs, num_outputs), requires_grad=True)\n",
    "b = torch.zeros(num_outputs, requires_grad=True)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "42145e9004629006",
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 3.6.2. 定义softmax操作"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "97d281d8940f32a2"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def softmax(X):\n",
    "    X_exp = torch.exp(X)\n",
    "    partition = X_exp.sum(1, keepdim=True)\n",
    "    return X_exp / partition  # 这里应用了广播机制"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "9345c750eea17b06",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "X = torch.normal(0, 1, (2, 5))\n",
    "X_prob = softmax(X)\n",
    "X_prob, X_prob.sum(1)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "2d27f20237a0d00c",
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 3.6.3. 定义模型"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "13cee6960d2333c0"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def net(X):\n",
    "    return softmax(torch.matmul(X.reshape((-1, W.shape[0])), W) + b)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5a2a69b5e1e9d905",
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 3.6.4. 定义损失函数"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1b46bb65c5836852"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "y = torch.tensor([0, 2])\n",
    "y_hat = torch.tensor([[0.1, 0.3, 0.6], [0.3, 0.2, 0.5]])\n",
    "y_hat[:, y], y_hat[[0, 1], y], y_hat"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a030115bad07b437",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def cross_entropy(y_hat, y):\n",
    "    return - torch.log(y_hat[range(len(y_hat)), y])\n",
    "\n",
    "\n",
    "cross_entropy(y_hat, y)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "f7eaa8b7c74db7c2",
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 3.6.5. 分类精度"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e2d8387e4442bdfe"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "y_hat.shape, len(y_hat.shape)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "f939c85411408039",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "y_hat = y_hat.argmax(dim=1)\n",
    "print(y, y_hat)\n",
    "cmp = y_hat.type(y.dtype) == y\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5ef1c622d7bdf4cc",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "float(cmp.type(y.dtype).sum())"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "cc828032e3e88d8d",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def accuracy(y_hat, y):  #@save\n",
    "    \"\"\"计算预测正确的数量\"\"\"\n",
    "    if len(y_hat.shape) > 1 and y_hat.shape[1] > 1:\n",
    "        y_hat = y_hat.argmax(axis=1)\n",
    "    cmp = y_hat.type(y.dtype) == y\n",
    "    return float(cmp.type(y.dtype).sum())"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "4cf206ca18e11a55",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "accuracy(y_hat, y) / len(y)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "d08780cbd4a171f5",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "from d2l.torch import Accumulator\n",
    "\n",
    "\n",
    "def evaluate_accuracy(net, data_iter):  #@save\n",
    "    \"\"\"计算在指定数据集上模型的精度\"\"\"\n",
    "    if isinstance(net, torch.nn.Module):\n",
    "        net.eval()  # 将模型设置为评估模式\n",
    "    metric = Accumulator(2)  # 正确预测数、预测总数\n",
    "    with torch.no_grad():\n",
    "        for X, y in data_iter:\n",
    "            metric.add(accuracy(net(X), y), y.numel())\n",
    "    return metric[0] / metric[1]"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "38d0671e9adb4e6d",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "evaluate_accuracy(net, test_iter)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e4db38bd1411c777",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def train_epoch_ch3(net, train_iter, loss, updater):\n",
    "    if isinstance(net, torch.nn.Module):\n",
    "        net.train()\n",
    "    metric = Accumulator(3)\n",
    "    for X, y in train_iter:\n",
    "        y_hat = net(X)\n",
    "        l = loss(y_hat, y)\n",
    "        if isinstance(updater, torch.optim.Optimizer):\n",
    "            updater.zero_grad()\n",
    "            l.mean().backward()\n",
    "            updater.step()\n",
    "        else:\n",
    "            l.sum().backward()\n",
    "            updater(X.shape[0])\n",
    "        metric.add(float(l.sum()), accuracy(y_hat, y), y.numel())\n",
    "    return metric[0] / metric[2], metric[1] / metric[2]"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "f917991d15231497",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "class Animator:  #@save\n",
    "    \"\"\"在动画中绘制数据\"\"\"\n",
    "\n",
    "    def __init__(self, xlabel=None, ylabel=None, legend=None, xlim=None,\n",
    "                 ylim=None, xscale='linear', yscale='linear',\n",
    "                 fmts=('-', 'm--', 'g-.', 'r:'), nrows=1, ncols=1,\n",
    "                 figsize=(3.5, 2.5)):\n",
    "        # 增量地绘制多条线\n",
    "        if legend is None:\n",
    "            legend = []\n",
    "        d2l.use_svg_display()\n",
    "        self.fig, self.axes = d2l.plt.subplots(nrows, ncols, figsize=figsize)\n",
    "        if nrows * ncols == 1:\n",
    "            self.axes = [self.axes, ]\n",
    "        # 使用lambda函数捕获参数\n",
    "        self.config_axes = lambda: d2l.set_axes(\n",
    "            self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)\n",
    "        self.X, self.Y, self.fmts = None, None, fmts\n",
    "\n",
    "    def add(self, x, y):\n",
    "        # 向图表中添加多个数据点\n",
    "        if not hasattr(y, \"__len__\"):\n",
    "            y = [y]\n",
    "        n = len(y)\n",
    "        if not hasattr(x, \"__len__\"):\n",
    "            x = [x] * n\n",
    "        if not self.X:\n",
    "            self.X = [[] for _ in range(n)]\n",
    "        if not self.Y:\n",
    "            self.Y = [[] for _ in range(n)]\n",
    "        for i, (a, b) in enumerate(zip(x, y)):\n",
    "            if a is not None and b is not None:\n",
    "                self.X[i].append(a)\n",
    "                self.Y[i].append(b)\n",
    "        self.axes[0].cla()\n",
    "        for x, y, fmt in zip(self.X, self.Y, self.fmts):\n",
    "            self.axes[0].plot(x, y, fmt)\n",
    "        self.config_axes()\n",
    "        display.display(self.fig)\n",
    "        display.clear_output(wait=True)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "babbf24a0e4cfc77",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def train_ch3(net, train_iter, test_iter, loss, num_epochs, updater):\n",
    "    animator = Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0.3, 0.9],\n",
    "                        legend=['train loss', 'train acc', 'test acc'])\n",
    "    for epoch in range(num_epochs):\n",
    "        train_metrics = train_epoch_ch3(net, train_iter, loss, updater)\n",
    "        test_acc = evaluate_accuracy(net, test_iter)\n",
    "        animator.add(epoch + 1, train_metrics + (test_acc,))\n",
    "    train_loss, train_acc = train_metrics\n",
    "    assert train_loss < 0.5, train_loss\n",
    "    assert 1 >= train_acc > 0.7, train_acc\n",
    "    assert 1 >= test_acc > 0.7, test_acc"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1b0e71888a3646fd",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "lr = 0.1\n",
    "\n",
    "\n",
    "def updater(batch_size):\n",
    "    return d2l.sgd([W, b], lr, batch_size)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "9e23175b0959bc69",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "num_epochs = 10\n",
    "train_ch3(net, train_iter, test_iter, cross_entropy, num_epochs, updater)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1d1d5d18b8bd706a",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "W.var(dim=0)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "290c820ed635995",
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 3.6.7. 预测"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a89241f4945cbc04"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def predict_ch3(net, test_iter, n=6):\n",
    "    for X, y in test_iter:\n",
    "        break\n",
    "    trues = d2l.get_fashion_mnist_labels(y)\n",
    "    preds = d2l.get_fashion_mnist_labels(net(X).argmax(axis=1))\n",
    "    titles = [true + '\\n' + pred for true, pred in zip(trues, preds)]\n",
    "    d2l.show_images(X[0:n].reshape((n, 28, 28)), 1, n, titles=titles[0:n])\n",
    "\n",
    "\n",
    "predict_ch3(net, test_iter)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-28T14:19:58.293629Z",
     "start_time": "2024-03-28T14:19:55.150886Z"
    }
   },
   "id": "afe3be30b7251158",
   "execution_count": 37
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "edb4e642c2f4dcc"
  }
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